Enhancing in-season yield forecast accuracy for film-mulched wheat: A hybrid approach coupling crop model and UAV remote-sensing data by ensemble learning technique

Abstract

Accurate in-season yield forecasts for field-scale crops are crucial for both farmers and decision-makers. Common methods for yield prediction are limited by the availability of unknown weather data (process-based crop models) and the failure to consider yield formation processes (statistical models based on unmanned aerial vehicle (UAV) images), respectively. Furthermore, previous studies focused only on crops without mulching, yet mulching is an important agronomic approach to increase grain yield in the arid areas of northwest China. We aim to develop a hybrid approach coupling crop model and UAV data through ensemble learning to achieve in-season yield forecasts for film-mulched wheat. A four-year field experiment was constructed (2018–2020 and 2021–2023). We first calibrated AquaCrop using data from 2018 to 2020, and historical weather data were employed to drive AquaCrop for predicting yields in 2021–2023. Next, statistical models were constructed to predict yields based on spectral and textural indices calculated from UAV images. Finally, a hybrid approach coupling the AquaCrop model and remote-sensing data was developed using ensemble learning technique. Quantifying the relative contribution of features used SHapley Additive exPlanations values. The results indicated that AquaCrop yield forecasts exhibited considerable uncertainties (R2: 0.53–0.63; NRMSE: 16.54%–14.83%). The interpretation of yield for remote-sensing data was influenced by background and saturation effects, reaching its highest accuracy at the heading stage (R2 was 0.80, NRMSE was 11.88%). Ensemble learning demonstrated strong performance compared to machine learning algorithms. The coupling model combined the advantages of crop and statistical models by the ensemble learning algorithm, achieving accurate yield predictions more than 40 days before harvest (heading stage) based on AdaBoost regression (R2 was 0.88, NRMSE was 8.40%). The most important forecasting factors affecting yield prediction were the textural indices, followed by the AquaCrop simulated values. Overall, the coupled model showed good performance in predicting the in-season yield of film-mulched wheat, which provided new insights into farm-scale yield prediction. Further validation of the generalizability of the coupled model in different scenarios is required in the future to improve the applicability of the model in actual production practice.